\section{Conclusion}
\label{sec:con}

Much work has focused on mining evolving data, and most approaches
learn the latest model from the latest data.  The problem with these
approaches is that the learned model is always of low quality. In
this paper, we propose a clustering approach to find hidden concepts
that control data generation. Unlike traditional clustering methods
that are based on data similarity (measured by Euclidean distance,
e.g.), we use a general quality function that is decided by specific
applications. We propose a two step algorithm, which uses enhanced
dynamic programming and EM like methods for clustering. Experiments
show that in benchmark datasets, our approach achieves the highest
accuracy with lowest cost in comparison with the current best
approaches.
